The present invention relates generally to a method and system for orthopaedic surgical planning and more specifically to surgical planning based on an automated finite element analysis of a bone-implant system using a 3D medical image of a patient.
Orthopaedic implants and the bone they are implanted into often must endure large stresses from habitual and sporadic loading and consequently the implants sometimes fail or loosen. Biomechanical analysis of a bone-implant system is often performed as part of the design process for such implants and it has been proposed that patient-specific biomechanical analysis of bone-implant systems could be used clinically to improve surgical planning. The information derived from such analyses could aid the surgeon in choosing an optimal size and position of a given implant design for a particular patient, or even choose among different implant designs. With more elderly individuals undergoing various orthopaedic and spine surgical procedures, there is also a need to assess a structural integrity of the underlying bone since it is possible that proposed bone is too weak to sustain the stresses that develop around the implant which could result in a loosening of the implant. Identifying patients having structurally insufficient bone as part of the surgical planning process enables surgeons to plan accordingly in the care of such patients, perhaps using a different size implant, an implant of a different design, augmenting the fixation of the implant using bone cement, biologics, or some other means, putting the patient on a bone-strengthening drug treatment, or even foregoing surgery.
Finite element modeling of a bone-implant system, based on the medical image of the patient (patient-specific), can be used for such purposes. While a number of references in the field have applied such finite element modeling techniques in research studies, for clinical implementation, a challenge is to automate this analysis to such a degree so that the analysis is available to a surgeon, radiologist, radiological imaging technician, or such other medical professional who has no technical expertise in finite element modeling. Further, the technique should be capable of being executed rapidly so that it can be performed intra-operatively or as the surgeon reviews the medical image; and the technique should be applicable to any bone geometry, shape, or size so that it can be applied confidently to any patient. There are no such techniques in the field for patient-specific modeling of bone-implant systems that meet all these criteria. Indeed, regarding clinical implementation of such patient-specific bone-implant finite element analyses, Helwig et al. recently reported that such analyses “are not yet possible in daily routine as an automatic algorithm for biomechanical assessment does not exist” (Helwig P, Faust G, Hindenlang U, Kroplin B, and Eingartnew C, Technology and Health Care 14: 411-419, 2006).
Patient-specific finite element models of orthopaedic bone-implant systems can be created from 3D CT scans. See for example, Keaveny T M and Bartel D L, Journal of Bone and Joint Surgery [Am], 77-A:911-923, 1995; Chen Si, Lin R M, Chang C H, Medical Engineering & Physics, 25:275282, 2003; Waide V, Cristofolini L, Stolk J, Verdonschot N, Boogaard G J, and Toni A, Journal of Biomechanics 37: 13-26, 2004. The general approach in these types of patient-specific finite element analyses has been to create a finite element mesh of the implant, and then build around that a compatible finite element mesh of the endosteal or periosteal surface of the bone. Each new bone to be analyzed requires a new finite element mesh of the bone to be constructed about the finite element mesh of the implant. Since this is a tedious process, this technique has never been applied to more than a few bones in a research setting. One problem with this approach is that it is difficult to automate in a clinical setting due to the great degree of population variability in the geometry of the bones of patients. This variability often requires an expert user to adjust the finite element mesh for the bone in order to avoid highly distorted finite elements, which are problematic numerically in the subsequent finite element analysis. Thus, while various methods for creation of such bone-implant meshes may work for regularly-sized and shaped bones, these algorithms typically require expert user input to deal with distorted elements in cases where the bone geometry is unusual or the implant geometry is complex. These techniques can therefore not be applied to large numbers of patients in an automated manner in near real-time by persons unskilled in finite element analysis.
Many of the same limitations are applicable to finite element meshes that are autopaved (i.e. automatically filled with 2D or 3D finite elements), either using tetrahedral, hexahedral, or some other type of finite element or a combination thereof. While automatic tetrahedral mesh generators can model the curvature of whole bones (see for example: GH Kwon, SW Chae, K J Lee, Computer and Structures 81:765-775, 2003; Viceconti M, Davinelli M, Taddei F, and Cappello A, Journal of Biomechanics 37: 1597-1605, 2004), the number of elements to do so in the presence of an implant would typically result in prohibitively large models that would be computationally very expensive to solve in a clinical context, particularly when one is to capture such implant detail as screw threads in the finite element mesh and small gaps between the implant and bone. For example, Tai et al. used automatic paving with 10-noded tetrahedral elements in a model of a proximal femur with stemless prosthesis. They reported that “the threads and the tips of the fixation screws were not modeled in the FEM in order to simplify the model setup” (Tai C L, Shih C H, Chen W P, Lee S S, Kiu Y L, Hsieh P H, and Chen W J, Clinical Biomechanics 18:S53-S58, 2003.) Even so, they needed more than 20,000 10-noded tetrahedral elements and had to solve the resulting analysis on a supercomputer.
When using such autopaving, mesh distortion can also occur around the edges of some bones, particularly in regions of degenerative changes where the bone geometry becomes very irregular. Mesh distortion is also an issue for use with deformable registration techniques, in which a “master” mesh of bone-implant system is “deformed” mathematically to fit the geometry of a digital image of a bone. For example, Couteau et al. reported that about 15% of the elements in their deformable registration-based meshing technique were distorted, according to standard measures of element distortion (Couteau B, Payan Y, and Lavallee S, Journal of Biomechanics 33: 1005-1009, 2000). None of the 10 cadaver femurs in that study showed any signs of pathology and there were no implants modeled. In a clinical situation, for certain patients with unusual bone geometries due to various types of pathology, highly distorted elements will inevitably result from such an automated meshing technique—especially when applied to a bone-implant situation. As a result of these limitations, none of these prior art techniques have found any clinical use for bone-implant surgical planning purposes and there are no commercial applications of any such techniques that are suitable for clinical use.
One particular type of autopaving technique that has been used in research studies for analysis of orthopaedic bone-implant systems is the “voxel-based” technique, in which every finite element in the mesh is cube-shaped and derived directly from the underlying CT image. However, this voxel-based approach provides a poor description of the surface of the implant because the geometry of the implant surface is forced to conform with the cube shape of the voxels. Thus, this approach does not work well for curved implants, flat implants that are placed oblique to the coordinate system of the cube-shaped voxels, or any implants that contain complex non-voxel geometric features such as screw threads, fenestrations, holes, filets and the like. Since maximum stresses in the implant typically occur on its surface and around such complex features, and since bone-implant interface stresses are key to assessing implant loosening, the use of such voxel-based meshes at the bone-implant interface is not desirable. Described originally in the early 1990's (see, for example, Skinner H B, Kim A S, Keyak J H, Mote C D: “Femoral prosthesis implantation induces changes in bone stress that depend on the extent of porous coating.” Journal of Orthopaedic Research 12:553-563, 1994), the voxel-based technique has not since been modified for improved performance and has not found any clinical usage.
As a result of the above limitations with the currently available technology, there remains a need for a simple, general method that accurately models the bone-implant interface and surrounding tissue and that in application can be applied rapidly and in a fully automated fashion in a clinical setting such that it can be used reliably for any patient without the need for any technical expertise in finite element modeling or mesh creation. The technique should also be applicable to many different types of implants and to situations when more than one implant is placed in the same bone, such as a plate with screws.
One additional issue that challenges full automation for clinical usage is the initial positioning and sizing of the implant within the bone. Given the complex 3D nature of most bones, and the heterogeneity in bone density both with any bone from a given patient and across different patients, it is oftentimes difficult to properly size and position a given implant design for a given patient, and in some cases it is even difficult to choose between different implant designs for a given patient since some implants might work better in certain patients but worse in others due to the complex bone-implant load-transfer characteristics associated with the 3D nature of the bone geometry and the spatially varying distribution of bone density. While techniques exist for image-based surgical planning and implant placement from medical images (see for example, U.S. Pat. Nos. 7,542,791 and 7,134,874), it would be desirable to have a technique that helps optimize the sizing and positioning of an implant for a given patient based on a finite element analysis of the implant in that patient—and to do so in an automated, cost-effective manner that would be feasible clinically both as pre-operative surgical planning and intra-operatively. Automation is crucial when the system is to be used by the surgeon, speed is critical when the system is to be used intra-operatively, and generality is required when the technique is to be applicable to any patient.